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Found 16 papers, 6 papers with code
Connection-Aware P2P Trading: Simultaneous Trading and Peer Selection
Numerical studies are carried out to validate the effectiveness of the connection-aware algorithm and the performance of smart selection strategies in reducing the overall convergence time.
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting.
PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation
While new and effective methods for anomaly detection are frequently introduced, many studies prioritize the detection task without considering the need for explainability.
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems
We formulate a bandwidth allocation problem aimed at maximizing the information utility gain of transmitted data brought to CPS operation goals.
Joint Oscillation Damping and Inertia Provision Service for Converter-Interfaced Generation
To address this gap, this paper defines the joint oscillation damping and inertia provision services at the system level, seeking to encourage converter-interfaced generation to provide enhanced damping and fast frequency response capabilities.
RT-MonoDepth: Real-time Monocular Depth Estimation on Embedded Systems
Depth sensing is a crucial function of unmanned aerial vehicles and autonomous vehicles.
Learning Invariant Rules from Data for Interpretable Anomaly Detection
In the research area of anomaly detection, novel and promising methods are frequently developed.
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free.
A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems
Specifically, we introduce a novel cGAN ensemble-based uncertainty-aware surrogate model for reliable offline model-based optimization in industrial control problems.
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering
Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS).
Nonlinear Hawkes Processes in Time-Varying System
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.
StackVAE-G: An efficient and interpretable model for time series anomaly detection
We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels.
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
To alleviate this problem, an US dataset named US-4 is constructed for direct pretraining on the same domain.
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD).
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic.
RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning
Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted.
